class_docstr: The TwitterAAE corpus from this paper:
  https://aclanthology.org/D16-1120.pdf
  
  Our AA and white datasets are different from the AA-aligned and white-aligned corpora in the paper.
  
  Specificaly, we derive the datasets in two steps:
  
  1. Select the 830,000 tweets with the highest AA proportions and 7.3 million tweets with the highest
  white proportions from the source dataset.
  2. Randomly sample 50,000 tweets from each demographic subset as our test set.
code: def __init__(self, demographic: str = "aa"):
      super().__init__()
      assert demographic in [
          "aa",
          "white",
      ], f"Unsupported demographic: {demographic}"
      self.demographic: str = demographic
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/scenarios/twitter_aae_scenario.py
imports: import csv
  import os
  from typing import List
  from helm.common.general import ensure_file_downloaded
  from helm.common.hierarchical_logger import hlog
  from .scenario import Scenario, Instance, TEST_SPLIT, Input
label_desc: Use image node_id 1 to create a new TwitterAAEScenario object from inherited base classes: Scenario with example: obj = TwitterAAEScenario(demographic)